




'''simple docstring'''


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   "▁"

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/s2t-small-librispeech-asr": 10_24,
}

UpperCAmelCase_					=   ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

UpperCAmelCase_					=   {"mustc": MUSTC_LANGS}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   MAX_MODEL_INPUT_SIZES
   _a    						=   ["""input_ids""", """attention_mask"""]

   _a    						=   []







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase="<s>"   ,				UpperCamelCase="</s>"   ,				UpperCamelCase="<pad>"   ,				UpperCamelCase="<unk>"   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     None:
     __a								=       {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				pad_token=UpperCamelCase   ,				do_upper_case=UpperCamelCase   ,				do_lower_case=UpperCamelCase   ,				tgt_lang=UpperCamelCase   ,				lang_codes=UpperCamelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**UpperCamelCase   ,				)
     __a								=       do_upper_case
     __a								=       do_lower_case

     __a								=       load_json(UpperCamelCase   )
     __a								=       {v: k for k, v in self.encoder.items()}
     __a								=       spm_file
     __a								=       load_spm(UpperCamelCase   ,				self.sp_model_kwargs   )

     if lang_codes is not None:
       __a								=       lang_codes
       __a								=       LANGUAGES[lang_codes]
       __a								=       [f"<lang:{lang}>" for lang in self.langs]
       __a								=       {lang: self.sp_model.PieceToId(f"<lang:{lang}>"   ) for lang in self.langs}

       __a								=       self.lang_tokens
       __a								=       tgt_lang if tgt_lang is not None else self.langs[0]

       self.set_tgt_lang_special_tokens(self._tgt_lang   )
     else:
       __a								=       {}







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return len(self.encoder   )







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return self._tgt_lang







   @tgt_lang.setter
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       new_tgt_lang
     self.set_tgt_lang_special_tokens(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       self.lang_code_to_id[tgt_lang]
     __a								=       [lang_code_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[str]:
     return self.sp_model.encode(UpperCamelCase   ,				out_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[int]:
     return self.encoder.get(UpperCamelCase   ,				self.encoder[self.unk_token]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     str:
     return self.decoder.get(UpperCamelCase   ,				self.unk_token   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     str:
     __a								=       []
     __a								=       ''
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         __a								=       self.sp_model.decode(UpperCamelCase   )
         out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
         __a								=       []
       else:
         current_sub_tokens.append(UpperCamelCase   )
     __a								=       self.sp_model.decode(UpperCamelCase   )
     out_string += decoded.upper() if self.do_upper_case else decoded
     return out_string.strip()







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[int]:
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = False   )					->     List[int]:

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=UpperCamelCase   ,				token_ids_a=UpperCamelCase   ,				already_has_special_tokens=UpperCamelCase   )

     __a								=       [1] * len(self.prefix_tokens   )
     __a								=       [1]
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase   )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase   )) + ([0] * len(UpperCamelCase   )) + suffix_ones







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.encoder.copy()
     vocab.update(self.added_tokens_encoder   )
     return vocab







   def __getstate__(						self   )					->     Dict:
     __a								=       self.__dict__.copy()
     __a								=       None
     return state







   def __setstate__(						self   ,				UpperCamelCase   )					->     None:
     __a								=       d

     # for backward compatibility
     if not hasattr(self   ,				'sp_model_kwargs'   ):
       __a								=       {}

     __a								=       load_spm(self.spm_file   ,				self.sp_model_kwargs   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     Tuple[str]:
     __a								=       Path(UpperCamelCase   )
     assert save_dir.is_dir(), f"{save_directory} should be a directory"
     __a								=       save_dir / (
         (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
     )
     __a								=       save_dir / (
         (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
     )

     save_json(self.encoder   ,				UpperCamelCase   )

     if os.path.abspath(self.spm_file   ) != os.path.abspath(UpperCamelCase   ) and os.path.isfile(self.spm_file   ):
       copyfile(self.spm_file   ,				UpperCamelCase   )
     elif not os.path.isfile(self.spm_file   ):
       with open(UpperCamelCase   ,				'wb'   ) as fi:
         __a								=       self.sp_model.serialized_model_proto()
         fi.write(UpperCamelCase   )

     return (str(UpperCamelCase   ), str(UpperCamelCase   ))


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Dict[str, Any]      ):
  __a								=       sentencepiece.SentencePieceProcessor(**a_      )
  spm.Load(str(a_      )      )
  return spm


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  with open(a_					,       'r'      ) as f:
    return json.load(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				str      ):
  with open(a_					,       'w'      ) as f:
    json.dump(a_					,       a_					,       indent=2      )









'''simple docstring'''


import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """segformer"""







   def __init__(						self   ,				UpperCamelCase=3   ,				UpperCamelCase=4   ,				UpperCamelCase=[2, 2, 2, 2]   ,				UpperCamelCase=[8, 4, 2, 1]   ,				UpperCamelCase=[32, 64, 160, 256]   ,				UpperCamelCase=[7, 3, 3, 3]   ,				UpperCamelCase=[4, 2, 2, 2]   ,				UpperCamelCase=[1, 2, 5, 8]   ,				UpperCamelCase=[4, 4, 4, 4]   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.02   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-6   ,				UpperCamelCase=256   ,				UpperCamelCase=255   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(**UpperCamelCase   )

     if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
           ' removed, as the behaviour will default to that of reshape_last_stage = True.'   ,				UpperCamelCase   ,				)

     __a								=       num_channels
     __a								=       num_encoder_blocks
     __a								=       depths
     __a								=       sr_ratios
     __a								=       hidden_sizes
     __a								=       patch_sizes
     __a								=       strides
     __a								=       mlp_ratios
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       classifier_dropout_prob
     __a								=       initializer_range
     __a								=       drop_path_rate
     __a								=       layer_norm_eps
     __a								=       decoder_hidden_size
     __a								=       kwargs.get('reshape_last_stage'   ,				UpperCamelCase   )
     __a								=       semantic_loss_ignore_index





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-4







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12



